OpenRaiser/Intern-Atlas huggingface dataset:
Comparisons of wavelet functions in QRS signal to noise ratio enhancement and detection accuracy
Venue: arXiv cs.CV 2015
Year: 2015

We compare the capability of wavelet functions used for noise removal in preprocessing step of a QRS detection algorithm in the electrocardiogram (ECG) signal. The QRS signal to noise ratio enhancement and the detection accuracy of each wavelet function are evaluated using three measures: (1) the ratio of the maximum beat amplitude to the minimum beat amplitude (RMM), (2) the mean of absolute of time error (MATE), and (3) the figure of merit (FOM). Three wavelet functions from previous well-known publications are explored, i.e., Bior1.3, Db10, and Mexican hat wavelet functions. Results evaluated with the ECG signal from MIT-BIH arrhythmia database show that the Mexican hat wavelet function is better than the others. While the scale 8 of Mexican hat wavelet function can provide the best enhancement in QRS signal to noise ratio, the scale 4 of Mexican hat wavelet function can provide the best detection accuracy. These results may be combined and may enable the use of a single fixed threshold for all ECG records leading to the reduction in computational complexity of the QRS detection algorithm.
OBDDs and (Almost) $k$-wise Independent Random Variables
Venue: arXiv cs.DS 2015
Year: 2015

OBDD-based graph algorithms deal with the characteristic function of the edge set E of a graph $G = (V,E)$ which is represented by an OBDD and solve optimization problems by mainly using functional operations. We present an OBDD-based algorithm which uses randomization for the first time. In particular, we give a maximal matching algorithm with $O(\log^3 \vert V \vert)$ functional operations in expectation. This algorithm may be of independent interest. The experimental evaluation shows that this algorithm outperforms known OBDD-based algorithms for the maximal matching problem. In order to use randomization, we investigate the OBDD complexity of $2^n$ (almost) $k$-wise independent binary random variables. We give a OBDD construction of size $O(n)$ for $3$-wise independent random variables and show a lower bound of $2^{Ω(n)}$ on the OBDD size for $k \geq 4$. The best known lower bound was $Ω(2^n/n)$ for $k \approx \log n$ due to Kabanets. We also give a very simple construction of $2^n$ $(\varepsilon, k)$-wise independent binary random variables by constructing a random OBDD of width $O(n k^2/\varepsilon)$.
RIOT OS Paves the Way for Implementation of High-Performance MAC Protocols
Venue: arXiv cs.NI 2015
Year: 2015

Implementing new, high-performance MAC protocols requires real-time features, to be able to synchronize correctly between different unrelated devices. Such features are highly desirable for operating wireless sensor networks (WSN) that are designed to be part of the Internet of Things (IoT). Unfortunately, the operating systems commonly used in this domain cannot provide such features. On the other hand, "bare-metal" development sacrifices portability, as well as the mul-titasking abilities needed to develop the rich applications that are useful in the domain of the Internet of Things. We describe in this paper how we helped solving these issues by contributing to the development of a port of RIOT OS on the MSP430 microcontroller, an architecture widely used in IoT-enabled motes. RIOT OS offers rich and advanced real-time features, especially the simultaneous use of as many hardware timers as the underlying platform (microcontroller) can offer. We then demonstrate the effectiveness of these features by presenting a new implementation, on RIOT OS, of S-CoSenS, an efficient MAC protocol that uses very low processing power and energy.
An investigation of SUDOKU-inspired non-linear codes with local constraints
Venue: arXiv cs.IT 2015
Year: 2015

Codes with local permutation constraints are described. Belief propagation decoding is shown to require the computation of permanents, and trellis-based methods for computing the permanents are introduced. New insights into the asymptotic performance of such codes are presented. A universal encoder for codes with local constraints is introduced, and simulation results for two code structures, SUDOKU and semi-pandiagonal Latin squares, are presented.
Anatomy-specific classification of medical images using deep convolutional nets
Venue: arXiv cs.CV 2015
Year: 2015

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.
Segmentation of Subspaces in Sequential Data
Venue: arXiv cs.CV 2015
Year: 2015

We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include an additional penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral, video and motion capture data. Experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.
Multichannel sparse recovery of complex-valued signals using Huber's criterion
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. This requires careful characterization of robust complex-valued loss functions as well as Huber's criterion function for the multivariate sparse regression problem. We devise a greedy algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Unlike the conventional SNIHT method, our algorithm, referred to as HUB-SNIHT, is robust under heavy-tailed non-Gaussian noise conditions, yet has a negligible performance loss compared to SNIHT under Gaussian noise. Usefulness of the method is illustrated in source localization application with sensor arrays.
Towards a relation extraction framework for cyber-security concepts
Venue: arXiv cs.IR 2015
Year: 2015

In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we follow developments in semi-supervised Natural Language Processing and implement a bootstrapping algorithm for extracting security entities and their relationships from text. The algorithm requires little input data, specifically, a few relations or patterns (heuristics for identifying relations), and incorporates an active learning component which queries the user on the most important decisions to prevent drifting from the desired relations. Preliminary testing on a small corpus shows promising results, obtaining precision of .82.
Channel Modeling between Seaborne MIMO Radar and MIMO Cellular System
Venue: arXiv cs.IT 2015
Year: 2015

Spectrum sharing between radars and cellular systems is an emerging area of research. In this paper, we model channel between a seaborne MIMO radar and MIMO cellular system. We model a 2D channel to capture the azimuth aspect of the spectrum sharing scenario. Our channel modeling methodology allows MIMO radar to place accurate nulls in the azimuth location of base stations (BS), thus, protecting them from harmful radar interference. We use a projection based approach, where radar waveform is projected onto null space of channel, for mitigating radar interference to BSs. This is also known as an approach based on eigen-nulling which is different from spatial-nulling commonly employed by radars. We show through simulations that the proposed spatial channel model allows eigen-nulling which performs superior to traditional spatial-nulling for interference mitigation. The proposed channel model can be leveraged to use eigen-nulling that enhances target detection and beampattern resolution of MIMO radar while mitigating interference to BSs.
Control and Communication Protocols that Enable Smart Building Microgrids
Venue: arXiv cs.SY 2015
Year: 2015

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility.
DEFENDER: Detecting and Forecasting Epidemics using Novel Data-analytics for Enhanced Response
Venue: arXiv cs.SI 2015
Year: 2015

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, tracking of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness, syndromic case tracking and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease count tracking approach which leverages counts from multiple symptoms, which was found to improve the tracking of diseases by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model.
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Venue: arXiv cs.LG 2015
Year: 2015

We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex functions, and a simple nonsmooth convex regularizer. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample $b$ functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that it benefits from two speedup effects. First, we prove that as long as $b$ is below a certain threshold, we can reach any predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.
Advanced Discrete-Time Control Methods for Industrial Applications
Venue: arXiv cs.SY 2015
Year: 2015

This thesis focuses on developing advanced control methods for two industrial systems in discrete-time aiming to enhance their performance in delivering the control objectives as well as considering the practical aspects. The first part addresses wind power dispatch into the electricity network using a battery energy storage system (BESS). To manage the amount of energy sold to the electricity market, a novel control scheme is developed based on discrete-time model predictive control (MPC) to ensure the optimal operation of the BESS in the presence of practical constraints. The control scheme follows a decision policy to sell more energy at peak demand times and store it at off-peaks in compliance with the Australian National Electricity Market rules. The performance of the control system is assessed under different scenarios using actual wind farm and electricity price data in simulation environment. The second part considers the control of overhead crane systems for automatic operation. To achieve high-speed load transportation with high-precision and minimum load swings, a new modeling approach is developed based on independent joint control strategy which considers actuators as the main plant. The nonlinearities of overhead crane dynamics are treated as disturbances acting on each actuator. The resulting model enables us to estimate the unknown parameters of the system including coulomb friction constants. A novel load swing control is also designed based on passivity-based control to suppress load swings. Two discrete-time controllers are then developed based on MPC and state feedback control to track reference trajectories along with a feedforward control to compensate for disturbances using computed torque control and a novel disturbance observer. The practical results on an experimental overhead crane setup demonstrate the high performance of the designed control systems.
On the Cost and Benefit of Cooperation (Extended Version)
Venue: arXiv cs.IT 2015
Year: 2015

In a cooperative coding scheme, network nodes work together to achieve higher transmission rates. To obtain a better understanding of cooperation, we consider a model in which two transmitters send rate-limited descriptions of their messages to a "cooperation facilitator", a node that sends back rate-limited descriptions of the pair to each transmitter. This model includes the conferencing encoders model and a prior model from the current authors as special cases. We show that except for a special class of multiple access channels, the gain in sum-capacity resulting from cooperation under this model is quite large. Adding a cooperation facilitator to any such channel results in a network that does not satisfy the edge removal property. An important special case is the Gaussian multiple access channel, for which we explicitly characterize the sum-rate cooperation gain.
STC: Coarse-Grained Vehicular Data Based Travel Speed Sensing by Leveraging Spatial-Temporal Correlation
Venue: arXiv cs.OH 2015
Year: 2015

As an important information for traffic condition evaluation, trip planning, transportation management, etc., average travel speed for a road means the average speed of vehicles travelling through this road in a given time duration. Traditional ways for collecting travel-speed oriented traffic data always depend on dedicated sensors and supporting infrastructures, and are therefore financial costly. Differently, vehicular crowdsensing as an infrastructure-free way, can be used to collect data including real-time locations and velocities of vehicles for road travel speed estimation, which is a quite low-cost way. However, vehicular crowdsensing data is always coarse-grained. This coarseness can lead to the incompleteness of travel speeds. Aiming to handle this problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC that exploits the spatial-temporal correlation among travel speeds for roads by introducing the time-lagged cross correlation function. The time lagging factor describes the time consumption of traffic feature diffusion along roads. To properly calculate cross correlation, we novelly make the determination of the time lagging factor self-adaptive by recording the locations of vehicles at different roads. Then, utilizing the local stationarity of cross correlation, we further reduce the problem of single-road travel speed vacancy completion to a minimization problem. Finally, we fill all the vacancies of travel speed for roads in a recursive way using the geometric structure of road net. Elaborate experiments based on real taxi trace data show that STC can settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed better, in comparison with representative existing methods such as KNN, Kriging and ARIMA.
Probabilistic Handshake in All-to-all Broadcast Coded Slotted ALOHA
Venue: arXiv cs.IT 2015
Year: 2015

We propose a probabilistic handshake mechanism for all-to-all broadcast coded slotted ALOHA. We consider a fully connected network where each user acts as both transmitter and receiver in a half-duplex mode. Users attempt to exchange messages with each other and to establish one-to-one handshakes, in the sense that each user decides whether its packet was successfully received by the other users: After performing decoding, each user estimates in which slots the resolved users transmitted their packets and, based on that, decides if these users successfully received its packet. The simulation results show that the proposed handshake algorithm allows the users to reliably perform the handshake. The paper also provides some analytical bounds on the performance of the proposed algorithm which are in good agreement with the simulation results.
A Reconfigurable Vector Instruction Processor for Accelerating a Convection Parametrization Model on FPGAs
Venue: arXiv cs.AR 2015
Year: 2015

High Performance Computing (HPC) platforms allow scientists to model computationally intensive algorithms. HPC clusters increasingly use General-Purpose Graphics Processing Units (GPGPUs) as accelerators; FPGAs provide an attractive alternative to GPGPUs for use as co-processors, but they are still far from being mainstream due to a number of challenges faced when using FPGA-based platforms. Our research aims to make FPGA-based high performance computing more accessible to the scientific community. In this work we present the results of investigating the acceleration of a particular atmospheric model, Flexpart, on FPGAs. We focus on accelerating the most computationally intensive kernel from this model. The key contribution of our work is the architectural exploration we undertook to arrive at a solution that best exploits the parallelism available in the legacy code, and is also convenient to program, so that eventually the compilation of high-level legacy code to our architecture can be fully automated. We present the three different types of architecture, comparing their resource utilization and performance, and propose that an architecture where there are a number of computational cores, each built along the lines of a vector instruction processor, works best in this particular scenario, and is a promising candidate for a generic FPGA-based platform for scientific computation. We also present the results of experiments done with various configuration parameters of the proposed architecture, to show its utility in adapting to a range of scientific applications.
The Nataf-Beta Random Field Classifier: An Extension of the Beta Conjugate Prior to Classification Problems
Venue: arXiv cs.LG 2015
Year: 2015

This paper presents the Nataf-Beta Random Field Classifier, a discriminative approach that extends the applicability of the Beta conjugate prior to classification problems. The approach's key feature is to model the probability of a class conditional on attribute values as a random field whose marginals are Beta distributed, and where the parameters of marginals are themselves described by random fields. Although the classification accuracy of the approach proposed does not statistically outperform the best accuracies reported in the literature, it ranks among the top tier for the six benchmark datasets tested. The Nataf-Beta Random Field Classifier is suited as a general purpose classification approach for real-continuous and real-integer attribute value problems.
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
Venue: arXiv cs.SD 2015
Year: 2015

Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate 'ideal' binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for 'karaoke' type applications.